notebook.community

Edit and run 





In [1]:



    


%pylab inline



    


















    






Populating the interactive namespace from numpy and matplotlib

















In [2]:



    


from imaginglss import DECALS
decals = DECALS('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2.conf.py')



    











In [3]:



    


from imaginglss.utils import mpl_aea
reload(mpl_aea)



    


















    
Out[3]:








<module 'imaginglss.utils.mpl_aea' from '/global/project/projectdirs/m779/yfeng1/source/imaginglss/imaginglss/utils/mpl_aea.pyc'>

















In [4]:



    


import h5py
import kdcount



    











In [5]:



    


from kdcount import sphere
from kdcount import correlate
reload(correlate)
reload(sphere)



    


















    
Out[5]:








<module 'kdcount.sphere' from '/global/homes/y/yfeng1/.local/lib/python2.7/site-packages/kdcount/sphere.pyc'>

















In [6]:



    


CDR_LRG = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/CDR_LRG.hdf5', 'r')
CDR_LRGR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/CDR_LRG-RANDOM.hdf5', 'r')



    











In [7]:



    


LSS_LRG = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LSS_LRG.hdf5', 'r')
LSS_LRGR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LSS_LRG-RANDOM.hdf5', 'r')



    











In [8]:



    


LRG = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LRG.hdf5', 'r')
LRGR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LRG-RANDOM.hdf5', 'r')



    











In [9]:



    


ELG = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/ELG.hdf5', 'r')
ELGR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/ELG-RANDOM.hdf5', 'r')



    











In [10]:



    


CDR_ELG = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/CDR_ELG.hdf5', 'r')
CDR_ELGR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/CDR_ELG-RANDOM.hdf5', 'r')



    











In [11]:



    


LSS_ELG = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LSS_ELG.hdf5', 'r')
LSS_ELGR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LSS_ELG-RANDOM.hdf5', 'r')



    











In [12]:



    


QSO = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/QSO.hdf5', 'r')
QSOR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/QSO-RANDOM.hdf5', 'r')



    











In [13]:



    


CDR_QSO = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/CDR_QSO.hdf5', 'r')
CDR_QSOR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/CDR_QSO-RANDOM.hdf5', 'r')



    











In [14]:



    


LSS_QSO = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LSS_QSO.hdf5', 'r')
LSS_QSOR = h5py.File('/global/homes/y/yfeng1/source/imaginglss/nersc/LSS_QSO-RANDOM.hdf5', 'r')



    











In [15]:



    


from imaginglss.model.dataproduct import bands



    











In [16]:



    


print bands



    


















    






{'Y': 5, 'W4': 9, 'r': 2, 'u': 0, 'W1': 6, 'g': 1, 'i': 3, 'W3': 8, 'z': 4, 'W2': 7}

















In [52]:



    


def readdata(h5file, completeness, subsample):
    RA = h5file['RA'][:]
    DEC = h5file['DEC'][:]
    FC = h5file['COMPLETENESS'][:]
    v = h5file['TYCHO_VETO']['BOSS_DR9'][:]
    
    mask = ~v & (FC >= completeness)
#    if 'CONFIDENCE' in h5file:
#        CONFIDENCE = h5file['CONFIDENCE'][:]
#        mask &= CONFIDENCE[:, 1] > 5
#        mask &= CONFIDENCE[:, 2] > 5
#        mask &= CONFIDENCE[:, 4] > 3
    print len(RA), mask.sum()
    print nanmin(22.5 - 2.5 * log10(3 * h5file['INTRINSIC_NOISELEVEL'][:, 2][mask]))
    print nanmin(22.5 - 2.5 * log10(3 * h5file['INTRINSIC_NOISELEVEL'][:, 2]))
    return RA[mask][::subsample], DEC[mask][::subsample]

def acorr(data, random, completeness, subsample):
    data = readdata(data, completeness, subsample)
    random = readdata(random, completeness, subsample)
    
    data = sphere.points(data[0], data[1])
    random = sphere.points(random[0], random[1])
    abin = sphere.AngularBinning(logspace(-3, 0, 16, endpoint=True))
    print len(data), len(random)
    
    DD = correlate.paircount(data, data, abin, np=8)
    DR = correlate.paircount(data, random, abin, np=8)
    RR = correlate.paircount(random, random, abin, np=8)
    
    r =  1. * len(data) / len(random)
    return abin, 1.0 * DD.sum1, 1.0 * DR.sum1 * r, 1.0 * RR.sum1 * (r * r)



    











In [53]:



    


print readdata(LSS_QSO, 1, 1)



    


















    






738222 666826
23.3046454088
-inf
(array([ 354.86732401,  354.78666713,  354.85074033, ...,  141.91069773,
        141.87000627,  141.87193209]), array([-0.12440392, -0.11047342, -0.10007411, ...,  7.01865182,
        7.01937541,  7.09722887]))

















In [54]:



    


print readdata(QSO, 1, 1)



    


















    






852123 752789
23.5546289414
-inf
(array([ 354.86732401,  354.78666713,  354.85074033, ...,  141.90837627,
        141.87193209,  141.87072541]), array([-0.12440392, -0.11047342, -0.10007411, ...,  7.04258183,
        7.09722887,  7.11407437]))

Martin's simulation, copied from email





In [19]:



    


xi_LRGsim = numpy.fromstring(
""" 0.0059      1.3333128      1.3339293      0.0066935
    0.0082      0.7937082      0.7941341      0.0063048
    0.0114      0.5210123      0.5213102      0.0046585
    0.0159      0.3251247      0.3249088      0.0028975
    0.0222      0.1872699      0.1874814      0.0025432
    0.0309      0.1099219      0.1098727      0.0013177
    0.0430      0.0710673      0.0711660      0.0010544
    0.0599      0.0553864      0.0553852      0.0007638
    0.0834      0.0449320      0.0449386      0.0005066
    0.1162      0.0374823      0.0375749      0.0006080
    0.1618      0.0302715      0.0302595      0.0004080
    0.2253      0.0231022      0.0230803      0.0004153
    0.3138      0.0166624      0.0166872      0.0003718
    0.4370      0.0117605      0.0117686      0.0002519
    0.6085      0.0077813      0.0077688      0.0002083
    0.8474      0.0045778      0.0045572      0.0001926
""", sep=' ').reshape(-1, 4).T



    











In [20]:



    


xi_ELGsim = numpy.fromstring(
""" 0.0059      0.0618821      0.0619034      0.0010241
    0.0082      0.0255468      0.0256541      0.0010897
    0.0114      0.0194393      0.0194185      0.0004137
    0.0159      0.0168435      0.0168256      0.0005362
    0.0222      0.0170369      0.0170354      0.0003401
    0.0309      0.0178402      0.0178283      0.0002898
    0.0430      0.0169437      0.0169424      0.0001548
    0.0599      0.0146951      0.0146951      0.0001200
    0.0834      0.0118795      0.0118697      0.0000833
    0.1162      0.0093799      0.0093856      0.0000966
    0.1618      0.0069754      0.0069720      0.0000785
    0.2253      0.0049743      0.0049652      0.0000645
    0.3138      0.0034435      0.0034394      0.0000554
    0.4370      0.0021931      0.0021899      0.0000463
    0.6085      0.0012434      0.0012403      0.0000511
    0.8474      0.0006279      0.0006284      0.0000397
""", sep=' ').reshape(-1, 4).T



    











In [21]:



    


xi_QSOsim = numpy.fromstring(
""" 0.0059      0.0172651      0.0173093      0.0058842
    0.0082     -0.0000735      0.0005288      0.0084870
    0.0114      0.0090166      0.0097537      0.0057657
    0.0159      0.0123315      0.0121035      0.0037471
    0.0222      0.0119125      0.0122433      0.0033194
    0.0309      0.0095169      0.0093196      0.0022516
    0.0430      0.0102863      0.0105746      0.0015901
    0.0599      0.0099424      0.0098718      0.0009474
    0.0834      0.0074437      0.0074153      0.0008298
    0.1162      0.0055929      0.0057222      0.0007914
    0.1618      0.0043447      0.0043344      0.0003325
    0.2253      0.0028906      0.0028863      0.0002219
    0.3138      0.0018112      0.0018252      0.0002027
    0.4370      0.0012336      0.0012337      0.0001401
    0.6085      0.0004966      0.0004931      0.0001145
    0.8474      0.0002425      0.0002461      0.0000798
""", sep=' ').reshape(-1, 4).T

Correlation functions from data





In [22]:



    


abin, CDR_LRG_DD, CDR_LRG_DR, CDR_LRG_RR = acorr(CDR_LRG, CDR_LRGR, 1.0, 1)



    


















    






21.0660038855
-inf
21.1136861845
-inf
539217 2948485

















In [23]:



    


abin, LSS_LRG_DD, LSS_LRG_DR, LSS_LRG_RR = acorr(LSS_LRG, LSS_LRGR, 1.0, 1)



    


















    






21.1011961033
-inf
21.1136861845
-inf
405557 3035802

















In [24]:



    


abin, LRG_DD, LRG_DR, LRG_RR = acorr(LRG, LRGR, 1.0, 1)



    


















    






21.1011961033
-inf
21.1136861845
-inf
664576 3035802

















In [25]:



    


plot(abin.angular_centers, (LRG_DD - 2 * LRG_DR + LRG_RR) / LRG_RR, label='FDR_LRG')
plot(abin.angular_centers, (CDR_LRG_DD - 2 * CDR_LRG_DR + CDR_LRG_RR) / CDR_LRG_RR, label='CDR_LRG')
plot(abin.angular_centers, (LSS_LRG_DD - 2 * LSS_LRG_DR + LSS_LRG_RR) / LSS_LRG_RR, label='LSS_LRG')
plot(xi_LRGsim[0], xi_LRGsim[1], 'x', label='Simulation LRG')
xlabel('theta')
ylabel('(DD - 2 DR + RR) / RR')
legend()
ylim(8e-3, 20)
loglog()



    


















    
Out[25]:








[]










    
























In [26]:



    


abin, ELG_DD, ELG_DR, ELG_RR = acorr(ELG, ELGR, 1.0, 1)



    


















    






21.1188687962
-inf
21.5834393167
-inf
2613060 1966257

















In [27]:



    


abin, CDR_ELG_DD, CDR_ELG_DR, CDR_ELG_RR = acorr(CDR_ELG, CDR_ELGR, 1.0, 1)



    


















    






23.099608746
-inf
23.0996124559
-inf
694838 1098701

















In [28]:



    


abin, LSS_ELG_DD, LSS_ELG_DR, LSS_ELG_RR = acorr(LSS_ELG, LSS_ELGR, 1.0, 1)



    


















    






21.1188687962
-inf
21.1136861845
-inf
2408861 2650562

















In [29]:



    


plot(abin.angular_centers, (ELG_DD - 2 * ELG_DR + ELG_RR) / ELG_RR, label='FDR ELG')
plot(abin.angular_centers, (CDR_ELG_DD - 2 * CDR_ELG_DR + CDR_ELG_RR) / CDR_ELG_RR, label='CDR ELG')
plot(abin.angular_centers, (LSS_ELG_DD - 2 * LSS_ELG_DR + LSS_ELG_RR) / LSS_ELG_RR, label='LSS ELG')
plot(xi_ELGsim[0], xi_ELGsim[1], 'x', label='Simulation')
xlabel('theta')
ylabel('(DD - 2 DR + RR) / RR')
legend()
xlim(1e-2, 1)
ylim(8e-3, 0.1)
loglog()



    


















    
Out[29]:








[]










    
























In [30]:



    


abin, CDR_QSO_DD, CDR_QSO_DR, CDR_QSO_RR = acorr(CDR_QSO, CDR_QSOR, 1.0, 1)



    


















    






23.297786215
-inf
23.2977843934
-inf
442971 854208

















In [31]:



    


abin, QSO_DD, QSO_DR, QSO_RR = acorr(QSO, QSOR, 1.0, 1)



    


















    






21.5628009931
-inf
21.1136861845
-inf
752789 2745167

















In [32]:



    


abin, LSS_QSO_DD, LSS_QSO_DR, LSS_QSO_RR = acorr(LSS_QSO, LSS_QSOR, 1.0, 1)



    


















    






21.5628009931
-inf
21.1136861845
-inf
666826 2800593

















In [33]:



    


plot(abin.angular_centers, (QSO_DD - 2 * QSO_DR + QSO_RR) / QSO_RR, label='FDR QSO')
plot(abin.angular_centers, (CDR_QSO_DD - 2 * CDR_QSO_DR + CDR_QSO_RR) / CDR_QSO_RR, label='CDR QSO')
plot(abin.angular_centers, (LSS_QSO_DD - 2 * LSS_QSO_DR + LSS_QSO_RR) / LSS_QSO_RR, label='LSS QSO')
plot(xi_QSOsim[0], xi_QSOsim[1], 'x', label='Simulation')
xlabel('theta')
ylabel('(DD - 2 DR + RR) / RR')
xlim(1e-2, 1)
ylim(1e-3, 10)
legend()
loglog()



    


















    
Out[33]:








[]

Now look at the spatial distribution of randoms and objects

See if this can explain the weird correlation functions we see.





In [34]:



    


reload(mpl_aea)



    


















    
Out[34]:








<module 'imaginglss.utils.mpl_aea' from '/global/project/projectdirs/m779/yfeng1/source/imaginglss/imaginglss/utils/mpl_aea.pyc'>

















In [35]:



    


ax = subplot(111, projection='aea')
ra, dec = ELGR['RA'][::1], ELGR['DEC'][::1]
v = ELGR['TYCHO_VETO']['BOSS_DR9'][:]
#_ = hist2d(ra, dec, bins=(200, 200)
ax.histmap(ra[v], dec[v], perarea=False, nside=128, range=((100, 400), (-10, 20)), vmin=0)
ax.set_xlim(100, 400)
ax.set_ylim(-10, 20)
ax.set_parallels(-20, 20)
ax.grid()
colorbar()



    


















    
Out[35]:








<matplotlib.colorbar.Colorbar at 0x7f3cd4639d90>










    
























In [36]:



    


ax = subplot(111, projection='aea')
ra, dec = ELGR['RA'][::1], ELGR['DEC'][::1]
v = ELGR['TYCHO_VETO']['BOSS_DR9'][:]
#_ = hist2d(ra, dec, bins=(200, 200)
ax.histmap(ra[v], dec[v], perarea=False, nside=128, range=((100, 400), (-10, 20)), vmin=0)
ax.set_xlim(100, 400)
ax.set_ylim(-10, 20)
ax.set_parallels(-20, 20)
ax.grid()
colorbar()



    


















    
Out[36]:








<matplotlib.colorbar.Colorbar at 0x7f3cd39a7850>










    
























In [37]:



    


ax = subplot(111, projection='aea')
ra, dec = LSS_ELGR['RA'][::1], LSS_ELGR['DEC'][::1]
v = LSS_ELGR['COMPLETENESS'][:] >= 0
#_ = hist2d(ra, dec, bins=(200, 200)
ax.histmap(ra[v], dec[v], perarea=False, nside=128, range=((320, 360), (-2, 2)), vmin=0)
ax.set_xlim(320, 360)
ax.set_ylim(-2, 2)
ax.set_parallels(-20, 20)
ax.grid()
colorbar()



    


















    
Out[37]:








<matplotlib.colorbar.Colorbar at 0x7f3cd32b3650>










    
























In [38]:



    


ax = subplot(111, projection='aea')
ra, dec = LSS_ELGR['RA'][::1], LSS_ELGR['DEC'][::1]
v = LSS_ELGR['COMPLETENESS'][:] ==1
#_ = hist2d(ra, dec, bins=(200, 200)
ax.histmap(ra[v], dec[v], perarea=False, nside=128, range=((320, 360), (-2, 2)), vmin=0)
ax.set_xlim(320, 360)
ax.set_ylim(-2, 2)
ax.set_parallels(-20, 20)
ax.grid()
colorbar()



    


















    
Out[38]:








<matplotlib.colorbar.Colorbar at 0x7f3cd36f2e90>










    
























In [39]:



    


def number_density(ax, cat):
    ra, dec = readdata(cat, 1.0, 1.0)
    #_ = hist2d(ra, dec, bins=(200, 200)
    ax.histmap(ra, dec, perarea=True, nside=64)
    ax.set_xlim(100, 400)
    ax.set_ylim(-10, 45)
    ax.set_parallels(-20, 20)
    ax.grid()



    











In [40]:



    


figure(figsize=(9, 6))
ax = subplot(231, projection='aea')
number_density(ax, LSS_QSOR)
colorbar()
ax.set_title('QSOR LSS')
ax = subplot(232, projection='aea')
number_density(ax, QSOR)
colorbar()
ax.set_title('QSOR FDR')
ax = subplot(233, projection='aea')
number_density(ax, CDR_QSOR)
colorbar()
ax.set_title('QSOR CDR')
ax = subplot(234, projection='aea')
number_density(ax, LSS_QSO)
ax.set_title('QSO LSS')
colorbar()
ax = subplot(235, projection='aea')
number_density(ax, QSO)
ax.set_title('QSO FDR')
colorbar()
ax = subplot(236, projection='aea')
number_density(ax, CDR_QSO)
ax.set_title('QSO CDR')
colorbar()



    


















    






21.1136861845
-inf
21.1136861845
-inf
23.2977843934
-inf
21.5628009931
-inf
21.5628009931
-inf
23.297786215
-inf










    
Out[40]:








<matplotlib.colorbar.Colorbar at 0x7f3cd2807950>










    
























In [41]:



    


figure(figsize=(9, 6))
ax = subplot(231, projection='aea')
number_density(ax, LSS_LRGR)
colorbar()
ax.set_title('LRGR LSS')
ax = subplot(232, projection='aea')
number_density(ax, LRGR)
colorbar()
ax.set_title('LRGR FDR')
ax = subplot(233, projection='aea')
number_density(ax, CDR_LRGR)
colorbar()
ax.set_title('LRGR CDR')
ax = subplot(234, projection='aea')
number_density(ax, LSS_LRG)
ax.set_title('LRG LSS')
colorbar()
ax = subplot(235, projection='aea')
number_density(ax, LRG)
ax.set_title('LRG FDR')
colorbar()
ax = subplot(236, projection='aea')
number_density(ax, CDR_LRG)
ax.set_title('LRG CDR')
colorbar()



    


















    






21.1136861845
-inf
21.1136861845
-inf
21.1136861845
-inf
21.1011961033
-inf
21.1011961033
-inf
21.0660038855
-inf










    
Out[41]:








<matplotlib.colorbar.Colorbar at 0x7f3cd188e150>










    
























In [42]:



    


figure(figsize=(9, 6))
ax = subplot(231, projection='aea')
number_density(ax, LSS_ELGR)
colorbar()
ax.set_title('ELGR LSS')
ax = subplot(232, projection='aea')
number_density(ax, ELGR)
colorbar()
ax.set_title('ELGR FDR')
ax = subplot(233, projection='aea')
number_density(ax, CDR_ELGR)
colorbar()
ax.set_title('ELGR CDR')
ax = subplot(234, projection='aea')
number_density(ax, LSS_ELG)
ax.set_title('ELG LSS')
colorbar()
ax = subplot(235, projection='aea')
number_density(ax, ELG)
ax.set_title('ELG FDR')
colorbar()
ax = subplot(236, projection='aea')
number_density(ax, CDR_ELG)
ax.set_title('ELG CDR')
colorbar()



    


















    






21.1136861845
-inf
21.5834393167
-inf
23.0996124559
-inf
21.1188687962
-inf
21.1188687962
-inf
23.099608746
-inf










    
Out[42]:








<matplotlib.colorbar.Colorbar at 0x7f3ccf6d5c10>

This Section compares with Martin's correlation function

This also shows for LRG the star veto is not doing much





In [43]:



    


RAd, DECd = loadtxt('/global/homes/y/yfeng1/source/imaginglss/nersc/ForYu/LRG_d.rdz', usecols=(0,1), unpack=True)



    











In [44]:



    


RAr, DECr = loadtxt('/global/homes/y/yfeng1/source/imaginglss/nersc/ForYu/LRG_r.rdz', usecols=(0,1), unpack=True)



    











In [45]:



    


data = sphere.points(RAd, DECd)
random = sphere.points(RAr, DECr)
abin = sphere.AngularBinning(logspace(-3, 0, 16, endpoint=True))
print len(data), len(random)

DD = correlate.paircount(data, data, abin, np=8)
DR = correlate.paircount(data, random, abin, np=8)
RR = correlate.paircount(random, random, abin, np=8)



    


















    






572573 1793492

















In [46]:



    


!cat /global/homes/y/yfeng1/source/imaginglss/nersc/ForYu/LRG.wt



    


















    






# MPI w(theta)-LS code running on 32 processes.
# Read    1793492 objects in 0.000<z<9.000 from LRG_r.rdz
# Read     572573 objects in 0.000<z<9.000 from LRG_d.rdz
# Read     572573 objects in 0.000<z<9.000 from LRG_d.rdz
# Reading and partitioning took 8.13 sec.
# Raw random load: [54496,57598].
# Raw random total count: 1793492
# Will count RR with random stride 1
# Using 32 pixels for bootstrap.
# Pixelizing took 0.26 sec.
# Using 32 pixels (528 pairs).
# After padding random load: [64327,98967].
# Tree has 225008 nodes.
# Memory load is 32.62 MB (excluding lists).
# Longest list length is 83
# Occurs for node of size 0.00
# Total cumulative list length is 361801
# Minimum node size is 1.12e-06 (min_node_size=1.00e-05).
# Pair calculation took 40.28 sec.
# Total run time 48.45 sec.
#  th(deg)          w(th)            Avg            Std
    0.0059        1.72780        1.71128        0.09555
    0.0082        1.14968        1.13562        0.09227
    0.0114        0.83225        0.81878        0.08563
    0.0159        0.56796        0.55760        0.06807
    0.0222        0.38498        0.37764        0.04687
    0.0309        0.29328        0.28751        0.04007
    0.0430        0.21880        0.21354        0.03293
    0.0599        0.16959        0.16552        0.02478
    0.0834        0.13219        0.12891        0.01880
    0.1162        0.10200        0.09932        0.01482
    0.1618        0.07478        0.07254        0.01106
    0.2253        0.05450        0.05247        0.00870
    0.3138        0.03873        0.03715        0.00619
    0.4370        0.02668        0.02557        0.00406
    0.6085        0.01798        0.01686        0.00307
    0.8474        0.01397        0.01290        0.00284

















In [ ]:



    






    











In [47]:



    


r = 1.0 * len(data) / len(random)
plot(abin.angular_centers, 
     (1.0 * DD.sum1 - 2 * r * DR.sum1 + r ** 2 * RR.sum1) / (r ** 2 * RR.sum1), 
     '+',
     label='Ellie data')
plot(abin.angular_centers, 
     (CDR_LRG_DD - 2 * CDR_LRG_DR + CDR_LRG_RR) / CDR_LRG_RR, 
     '+',
     label='CDR_LRG data no confidence', 
     )
r, w = loadtxt('/global/homes/y/yfeng1/source/imaginglss/nersc/ForYu/LRG.wt', usecols=(0, 1), unpack=True)

plot(r, w, 'x', label='Martin code Ellie data')
plot(xi_LRGsim[0], xi_LRGsim[1], label='simulation')

plot(abin.angular_centers, (LRG_DD - 2 * LRG_DR + LRG_RR) / LRG_RR, 'o', label='FDR_LRG data')
xlabel('theta')
ylabel('(DD - 2 DR + RR) / RR')
legend(loc='lower left')
loglog()



    


















    
Out[47]:








[]










    
























In [48]:



    


_ = hist2d(RAd, DECd, bins=(400, 400))
print len(RAd)



    


















    






572573










    
























In [188]:



    


RA_CDR_LRGd, DEC_CDR_LRGd = readdata(CDR_LRG, 1, 1)
print len(RA_CDR_LRGd)
_ = hist2d(RA_CDR_LRGd, DEC_CDR_LRGd, bins=(400, 400))



    


















    






539217










    
























In [187]:



    


RA_CDR_LRGr, DEC_CDR_LRGr = readdata(CDR_LRGR, 1, 1)
print len(RA_CDR_LRGr)
_ = hist2d(RA_CDR_LRGr, DEC_CDR_LRGr, bins=(400, 400))



    


















    






2948485










    
























In [ ]:



    






    











In [175]:



    


_ = hist2d(RAr, DECr, bins=(400, 400))



    


















    
























In [59]:



    


t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/ELG-wtheta.txt', unpack=True)
plot(t, w)
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/LRG-wtheta.txt', unpack=True)
plot(t, w)
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/QSO-wtheta.txt', unpack=True)
plot(t, w)

loglog()



    


















    
Out[59]:








[]










    
























In [64]:



    


t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRPP_ELG-wtheta.txt', unpack=True)
plot(t, w)
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRPP_LRG-wtheta.txt', unpack=True)
plot(t, w)
#t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRPP_QSO-wtheta.txt', unpack=True)
plot(t, w)

loglog()



    


















    
Out[64]:








[]










    
























In [77]:



    


t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRPP_LRG-wtheta.txt', unpack=True)
plot(t, w, label='FDRPP')
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRP_LRG-wtheta.txt', unpack=True)
plot(t, w, label='FDRP')
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/LRG-wtheta.txt', unpack=True)
plot(t, w, label='FDR')
loglog()
title('LRG')
legend()
xlabel('theta')
ylabel('w(theta)')



    


















    
Out[77]:








<matplotlib.text.Text at 0x7f3ccef62a90>










    
























In [78]:



    


t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRPP_ELG-wtheta.txt', unpack=True)
plot(t, w, label='FDRPP')
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRP_ELG-wtheta.txt', unpack=True)
plot(t, w, label='FDRP')
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/ELG-wtheta.txt', unpack=True)
plot(t, w, label='FDR')
loglog()
title('ELG')
legend()
xlabel('theta')
ylabel('w(theta)')



    


















    
Out[78]:








<matplotlib.text.Text at 0x7f3ccf0afdd0>










    
























In [79]:



    


t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRPP_QSO-wtheta.txt', unpack=True)
plot(t, w, label='FDRPP')
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/FDRP_QSO-wtheta.txt', unpack=True)
plot(t, w, label='FDRP')
t, w= loadtxt('/global/homes/y/yfeng1/m779/yfeng1/imaginglss/dr2lss/QSO-wtheta.txt', unpack=True)
plot(t, w, label='FDR')
loglog()
title('QSO')
legend()
xlabel('theta')
ylabel('w(theta)')



    


















    
Out[79]:








<matplotlib.text.Text at 0x7f3cce756b90>










    
























In [ ]:

Content source: bccp/imaginglss-notebooks

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